Overview

Dataset statistics

Number of variables24
Number of observations3664
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows357
Duplicate rows (%)9.7%
Total size in memory687.1 KiB
Average record size in memory192.0 B

Variable types

Numeric16
Categorical8

Alerts

url_chinese_present has constant value ""Constant
html_num_tags('applet') has constant value ""Constant
Dataset has 357 (9.7%) duplicate rowsDuplicates
url_len is highly overall correlated with url_path_len and 1 other fieldsHigh correlation
url_num_hyphens_dom is highly overall correlated with url_domain_len and 1 other fieldsHigh correlation
url_path_len is highly overall correlated with url_len and 1 other fieldsHigh correlation
url_domain_len is highly overall correlated with url_num_hyphens_dom and 1 other fieldsHigh correlation
url_hostname_len is highly overall correlated with url_num_hyphens_dom and 1 other fieldsHigh correlation
url_query_len is highly overall correlated with url_num_query_paraHigh correlation
url_num_query_para is highly overall correlated with url_query_lenHigh correlation
url_entropy is highly overall correlated with url_len and 1 other fieldsHigh correlation
html_num_tags('script') is highly overall correlated with html_num_tags('div') and 1 other fieldsHigh correlation
html_num_tags('object') is highly overall correlated with html_num_tags('embed')High correlation
html_num_tags('div') is highly overall correlated with html_num_tags('script') and 2 other fieldsHigh correlation
html_num_tags('form') is highly overall correlated with html_num_tags('div')High correlation
html_num_tags('a') is highly overall correlated with html_num_tags('script') and 1 other fieldsHigh correlation
html_num_tags('embed') is highly overall correlated with html_num_tags('object')High correlation
url_ip_present is highly imbalanced (66.9%)Imbalance
url_port is highly imbalanced (97.8%)Imbalance
html_num_tags('embed') is highly imbalanced (92.0%)Imbalance
html_num_tags('head') is highly imbalanced (91.8%)Imbalance
html_num_tags('body') is highly imbalanced (83.9%)Imbalance
html_num_tags('div') is highly skewed (γ1 = 45.20523454)Skewed
html_num_tags('a') is highly skewed (γ1 = 32.57211778)Skewed
url_num_hyphens_dom has 2734 (74.6%) zerosZeros
url_path_len has 626 (17.1%) zerosZeros
url_num_underscores has 3206 (87.5%) zerosZeros
url_query_len has 3436 (93.8%) zerosZeros
url_num_query_para has 3471 (94.7%) zerosZeros
html_num_tags('iframe') has 3172 (86.6%) zerosZeros
html_num_tags('script') has 486 (13.3%) zerosZeros
html_num_tags('object') has 3579 (97.7%) zerosZeros
html_num_tags('div') has 328 (9.0%) zerosZeros
html_num_tags('form') has 1191 (32.5%) zerosZeros
html_num_tags('a') has 672 (18.3%) zerosZeros

Reproduction

Analysis started2023-03-07 08:28:07.486248
Analysis finished2023-03-07 08:28:32.559433
Duration25.07 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

url_len
Real number (ℝ)

Distinct242
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.559225
Minimum6
Maximum1837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:32.631194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile15
Q124
median36
Q355
95-th percentile157
Maximum1837
Range1831
Interquartile range (IQR)31

Descriptive statistics

Standard deviation82.493265
Coefficient of variation (CV)1.5119948
Kurtosis176.17286
Mean54.559225
Median Absolute Deviation (MAD)14
Skewness10.797476
Sum199905
Variance6805.1387
MonotonicityNot monotonic
2023-03-07T17:28:32.733852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 147
 
4.0%
34 117
 
3.2%
39 101
 
2.8%
18 97
 
2.6%
37 91
 
2.5%
26 90
 
2.5%
23 84
 
2.3%
32 84
 
2.3%
24 83
 
2.3%
33 83
 
2.3%
Other values (232) 2687
73.3%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 2
 
0.1%
9 5
 
0.1%
10 5
 
0.1%
11 13
 
0.4%
12 20
 
0.5%
13 49
1.3%
14 65
1.8%
15 58
1.6%
16 70
1.9%
ValueCountFrequency (%)
1837 1
< 0.1%
1709 1
< 0.1%
1583 1
< 0.1%
1302 1
< 0.1%
1143 1
< 0.1%
1043 1
< 0.1%
952 1
< 0.1%
926 1
< 0.1%
880 1
< 0.1%
629 1
< 0.1%

url_num_hyphens_dom
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41293668
Minimum0
Maximum14
Zeros2734
Zeros (%)74.6%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:32.818568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84705763
Coefficient of variation (CV)2.0513015
Kurtosis22.93946
Mean0.41293668
Median Absolute Deviation (MAD)0
Skewness3.1968506
Sum1513
Variance0.71750663
MonotonicityNot monotonic
2023-03-07T17:28:32.889014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 2734
74.6%
1 509
 
13.9%
2 315
 
8.6%
3 71
 
1.9%
4 26
 
0.7%
5 5
 
0.1%
6 3
 
0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 2734
74.6%
1 509
 
13.9%
2 315
 
8.6%
3 71
 
1.9%
4 26
 
0.7%
5 5
 
0.1%
6 3
 
0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
6 3
 
0.1%
5 5
 
0.1%
4 26
 
0.7%
3 71
 
1.9%
2 315
 
8.6%
1 509
 
13.9%
0 2734
74.6%

url_path_len
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct203
Distinct (%)5.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean29.36582
Minimum0
Maximum1816
Zeros626
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:32.986171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median13
Q331
95-th percentile96
Maximum1816
Range1816
Interquartile range (IQR)30

Descriptive statistics

Standard deviation78.595248
Coefficient of variation (CV)2.6764193
Kurtosis218.09471
Mean29.36582
Median Absolute Deviation (MAD)12
Skewness12.49738
Sum107567
Variance6177.2129
MonotonicityNot monotonic
2023-03-07T17:28:33.085484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 626
 
17.1%
1 470
 
12.8%
10 306
 
8.4%
13 129
 
3.5%
17 129
 
3.5%
20 75
 
2.0%
22 72
 
2.0%
9 70
 
1.9%
11 67
 
1.8%
6 63
 
1.7%
Other values (193) 1656
45.2%
ValueCountFrequency (%)
0 626
17.1%
1 470
12.8%
2 3
 
0.1%
3 13
 
0.4%
4 21
 
0.6%
5 24
 
0.7%
6 63
 
1.7%
7 60
 
1.6%
8 41
 
1.1%
9 70
 
1.9%
ValueCountFrequency (%)
1816 1
< 0.1%
1690 1
< 0.1%
1566 1
< 0.1%
1286 1
< 0.1%
1127 1
< 0.1%
1022 1
< 0.1%
936 1
< 0.1%
910 1
< 0.1%
866 1
< 0.1%
607 1
< 0.1%

url_domain_len
Real number (ℝ)

Distinct67
Distinct (%)1.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20.383292
Minimum4
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:33.191132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q114
median17
Q324
95-th percentile37
Maximum109
Range105
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.5970119
Coefficient of variation (CV)0.47082737
Kurtosis15.568031
Mean20.383292
Median Absolute Deviation (MAD)4
Skewness2.805769
Sum74664
Variance92.102637
MonotonicityNot monotonic
2023-03-07T17:28:33.303750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 485
 
13.2%
13 267
 
7.3%
14 252
 
6.9%
15 213
 
5.8%
16 213
 
5.8%
18 192
 
5.2%
20 167
 
4.6%
12 152
 
4.1%
11 138
 
3.8%
21 130
 
3.5%
Other values (57) 1454
39.7%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 5
 
0.1%
6 6
 
0.2%
7 24
 
0.7%
8 10
 
0.3%
9 44
 
1.2%
10 50
 
1.4%
11 138
3.8%
12 152
4.1%
13 267
7.3%
ValueCountFrequency (%)
109 1
 
< 0.1%
104 2
0.1%
103 1
 
< 0.1%
101 4
0.1%
100 1
 
< 0.1%
85 1
 
< 0.1%
77 1
 
< 0.1%
74 2
0.1%
72 1
 
< 0.1%
68 1
 
< 0.1%

url_hostname_len
Real number (ℝ)

Distinct67
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.330513
Minimum4
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:33.407409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q114
median17
Q324
95-th percentile37
Maximum109
Range105
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.6280931
Coefficient of variation (CV)0.47357846
Kurtosis15.392418
Mean20.330513
Median Absolute Deviation (MAD)4
Skewness2.7904095
Sum74491
Variance92.700177
MonotonicityNot monotonic
2023-03-07T17:28:33.510064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 461
 
12.6%
13 284
 
7.8%
14 270
 
7.4%
15 219
 
6.0%
16 198
 
5.4%
18 185
 
5.0%
20 165
 
4.5%
12 156
 
4.3%
11 144
 
3.9%
21 130
 
3.5%
Other values (57) 1452
39.6%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 5
 
0.1%
6 6
 
0.2%
7 24
 
0.7%
8 10
 
0.3%
9 44
 
1.2%
10 50
 
1.4%
11 144
3.9%
12 156
4.3%
13 284
7.8%
ValueCountFrequency (%)
109 1
 
< 0.1%
104 2
0.1%
103 1
 
< 0.1%
101 4
0.1%
100 1
 
< 0.1%
85 1
 
< 0.1%
77 1
 
< 0.1%
74 2
0.1%
72 1
 
< 0.1%
68 1
 
< 0.1%

url_num_dots
Real number (ℝ)

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5169214
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:33.597770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum32
Range31
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5341193
Coefficient of variation (CV)0.60952212
Kurtosis74.78281
Mean2.5169214
Median Absolute Deviation (MAD)1
Skewness5.7030461
Sum9222
Variance2.3535219
MonotonicityNot monotonic
2023-03-07T17:28:33.674580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 1505
41.1%
3 952
26.0%
1 662
18.1%
4 383
 
10.5%
5 53
 
1.4%
6 50
 
1.4%
8 15
 
0.4%
7 13
 
0.4%
9 10
 
0.3%
11 5
 
0.1%
Other values (7) 16
 
0.4%
ValueCountFrequency (%)
1 662
18.1%
2 1505
41.1%
3 952
26.0%
4 383
 
10.5%
5 53
 
1.4%
6 50
 
1.4%
7 13
 
0.4%
8 15
 
0.4%
9 10
 
0.3%
10 5
 
0.1%
ValueCountFrequency (%)
32 1
 
< 0.1%
26 2
 
0.1%
16 1
 
< 0.1%
14 1
 
< 0.1%
13 5
 
0.1%
12 1
 
< 0.1%
11 5
 
0.1%
10 5
 
0.1%
9 10
0.3%
8 15
0.4%

url_num_underscores
Real number (ℝ)

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27265284
Minimum0
Maximum18
Zeros3206
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:33.753316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1245917
Coefficient of variation (CV)4.1246286
Kurtosis96.850391
Mean0.27265284
Median Absolute Deviation (MAD)0
Skewness8.5207928
Sum999
Variance1.2647065
MonotonicityNot monotonic
2023-03-07T17:28:33.829105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 3206
87.5%
1 257
 
7.0%
2 87
 
2.4%
3 57
 
1.6%
4 29
 
0.8%
6 7
 
0.2%
14 6
 
0.2%
5 5
 
0.1%
12 4
 
0.1%
10 2
 
0.1%
Other values (3) 4
 
0.1%
ValueCountFrequency (%)
0 3206
87.5%
1 257
 
7.0%
2 87
 
2.4%
3 57
 
1.6%
4 29
 
0.8%
5 5
 
0.1%
6 7
 
0.2%
10 2
 
0.1%
11 1
 
< 0.1%
12 4
 
0.1%
ValueCountFrequency (%)
18 2
 
0.1%
15 1
 
< 0.1%
14 6
 
0.2%
12 4
 
0.1%
11 1
 
< 0.1%
10 2
 
0.1%
6 7
 
0.2%
5 5
 
0.1%
4 29
0.8%
3 57
1.6%

url_query_len
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7076965
Minimum0
Maximum429
Zeros3436
Zeros (%)93.8%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:33.930719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile27.85
Maximum429
Range429
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.318285
Coefficient of variation (CV)5.3780622
Kurtosis85.203526
Mean4.7076965
Median Absolute Deviation (MAD)0
Skewness8.0545636
Sum17249
Variance641.01555
MonotonicityNot monotonic
2023-03-07T17:28:34.027564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3436
93.8%
41 50
 
1.4%
157 21
 
0.6%
5 12
 
0.3%
11 8
 
0.2%
44 6
 
0.2%
165 6
 
0.2%
45 5
 
0.1%
70 5
 
0.1%
36 4
 
0.1%
Other values (68) 111
 
3.0%
ValueCountFrequency (%)
0 3436
93.8%
5 12
 
0.3%
6 2
 
0.1%
9 1
 
< 0.1%
11 8
 
0.2%
13 1
 
< 0.1%
15 4
 
0.1%
16 3
 
0.1%
17 2
 
0.1%
18 2
 
0.1%
ValueCountFrequency (%)
429 2
0.1%
350 1
< 0.1%
312 1
< 0.1%
289 1
< 0.1%
271 1
< 0.1%
248 1
< 0.1%
208 1
< 0.1%
200 2
0.1%
185 1
< 0.1%
173 1
< 0.1%

url_num_query_para
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10425764
Minimum0
Maximum9
Zeros3471
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:34.113284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.57430977
Coefficient of variation (CV)5.5085629
Kurtosis93.849177
Mean0.10425764
Median Absolute Deviation (MAD)0
Skewness8.5653553
Sum382
Variance0.32983172
MonotonicityNot monotonic
2023-03-07T17:28:34.186162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 3471
94.7%
1 105
 
2.9%
2 41
 
1.1%
3 33
 
0.9%
6 5
 
0.1%
7 4
 
0.1%
9 2
 
0.1%
8 2
 
0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 3471
94.7%
1 105
 
2.9%
2 41
 
1.1%
3 33
 
0.9%
4 1
 
< 0.1%
6 5
 
0.1%
7 4
 
0.1%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
9 2
 
0.1%
8 2
 
0.1%
7 4
 
0.1%
6 5
 
0.1%
4 1
 
< 0.1%
3 33
 
0.9%
2 41
 
1.1%
1 105
 
2.9%
0 3471
94.7%

url_ip_present
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3441 
1.0
 
223

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10992
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3441
93.9%
1.0 223
 
6.1%

Length

2023-03-07T17:28:34.266208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T17:28:34.356905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3441
93.9%
1.0 223
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 7105
64.6%
. 3664
33.3%
1 223
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7328
66.7%
Other Punctuation 3664
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7105
97.0%
1 223
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 3664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7105
64.6%
. 3664
33.3%
1 223
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7105
64.6%
. 3664
33.3%
1 223
 
2.0%

url_entropy
Real number (ℝ)

Distinct2524
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2285684
Minimum2.7378394
Maximum5.8217821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:34.438907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.7378394
5-th percentile3.6197263
Q13.9831956
median4.1895611
Q34.4589405
95-th percentile4.9033082
Maximum5.8217821
Range3.0839426
Interquartile range (IQR)0.47574484

Descriptive statistics

Standard deviation0.3930554
Coefficient of variation (CV)0.092952357
Kurtosis0.69692091
Mean4.2285684
Median Absolute Deviation (MAD)0.22579503
Skewness0.30399941
Sum15493.475
Variance0.15449254
MonotonicityNot monotonic
2023-03-07T17:28:34.535924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.970175521 18
 
0.5%
4.084962501 14
 
0.4%
3.886842188 13
 
0.4%
3.97366069 12
 
0.3%
3.788754914 11
 
0.3%
3.938721876 11
 
0.3%
4.053508855 11
 
0.3%
3.558518613 10
 
0.3%
3.689703732 10
 
0.3%
4.168295834 9
 
0.2%
Other values (2514) 3545
96.8%
ValueCountFrequency (%)
2.737839416 1
< 0.1%
2.819808339 1
< 0.1%
2.971860874 1
< 0.1%
3.012015896 1
< 0.1%
3.019765516 1
< 0.1%
3.074515896 2
0.1%
3.077323802 1
< 0.1%
3.103701696 1
< 0.1%
3.127986807 1
< 0.1%
3.137015896 1
< 0.1%
ValueCountFrequency (%)
5.821782065 1
< 0.1%
5.815521588 1
< 0.1%
5.676410099 1
< 0.1%
5.65563894 1
< 0.1%
5.64727204 1
< 0.1%
5.645037892 1
< 0.1%
5.624739025 2
0.1%
5.612819605 1
< 0.1%
5.567501007 1
< 0.1%
5.566057159 1
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3664 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10992
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3664
100.0%

Length

2023-03-07T17:28:34.630605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T17:28:34.721306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3664
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7328
66.7%
. 3664
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7328
66.7%
Other Punctuation 3664
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7328
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7328
66.7%
. 3664
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7328
66.7%
. 3664
33.3%

url_port
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3656 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10992
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3656
99.8%
1.0 8
 
0.2%

Length

2023-03-07T17:28:34.790072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T17:28:34.875787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3656
99.8%
1.0 8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 7320
66.6%
. 3664
33.3%
1 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7328
66.7%
Other Punctuation 3664
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7320
99.9%
1 8
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 3664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7320
66.6%
. 3664
33.3%
1 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7320
66.6%
. 3664
33.3%
1 8
 
0.1%

html_num_tags('iframe')
Real number (ℝ)

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22079694
Minimum0
Maximum26
Zeros3172
Zeros (%)86.6%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:34.939576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.89838258
Coefficient of variation (CV)4.068818
Kurtosis254.94884
Mean0.22079694
Median Absolute Deviation (MAD)0
Skewness12.35457
Sum809
Variance0.80709126
MonotonicityNot monotonic
2023-03-07T17:28:35.021298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 3172
86.6%
1 365
 
10.0%
2 59
 
1.6%
3 41
 
1.1%
4 8
 
0.2%
5 6
 
0.2%
8 3
 
0.1%
12 2
 
0.1%
6 2
 
0.1%
10 2
 
0.1%
Other values (4) 4
 
0.1%
ValueCountFrequency (%)
0 3172
86.6%
1 365
 
10.0%
2 59
 
1.6%
3 41
 
1.1%
4 8
 
0.2%
5 6
 
0.2%
6 2
 
0.1%
7 1
 
< 0.1%
8 3
 
0.1%
10 2
 
0.1%
ValueCountFrequency (%)
26 1
 
< 0.1%
17 1
 
< 0.1%
12 2
 
0.1%
11 1
 
< 0.1%
10 2
 
0.1%
8 3
 
0.1%
7 1
 
< 0.1%
6 2
 
0.1%
5 6
0.2%
4 8
0.2%

html_num_tags('script')
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7854803
Minimum0
Maximum267
Zeros486
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:35.113993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q312
95-th percentile30
Maximum267
Range267
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.647356
Coefficient of variation (CV)1.4395748
Kurtosis69.093899
Mean8.7854803
Median Absolute Deviation (MAD)4
Skewness5.5378516
Sum32190
Variance159.95561
MonotonicityNot monotonic
2023-03-07T17:28:35.216659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 646
17.6%
0 486
13.3%
1 355
 
9.7%
3 232
 
6.3%
9 184
 
5.0%
8 172
 
4.7%
4 153
 
4.2%
17 133
 
3.6%
5 121
 
3.3%
6 109
 
3.0%
Other values (68) 1073
29.3%
ValueCountFrequency (%)
0 486
13.3%
1 355
9.7%
2 646
17.6%
3 232
 
6.3%
4 153
 
4.2%
5 121
 
3.3%
6 109
 
3.0%
7 100
 
2.7%
8 172
 
4.7%
9 184
 
5.0%
ValueCountFrequency (%)
267 1
< 0.1%
174 1
< 0.1%
140 1
< 0.1%
129 2
0.1%
108 1
< 0.1%
104 1
< 0.1%
98 1
< 0.1%
97 1
< 0.1%
90 1
< 0.1%
87 2
0.1%

html_num_tags('embed')
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3602 
1.0
 
60
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10992
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3602
98.3%
1.0 60
 
1.6%
3.0 2
 
0.1%

Length

2023-03-07T17:28:35.462644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T17:28:35.550351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3602
98.3%
1.0 60
 
1.6%
3.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7266
66.1%
. 3664
33.3%
1 60
 
0.5%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7328
66.7%
Other Punctuation 3664
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7266
99.2%
1 60
 
0.8%
3 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 3664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7266
66.1%
. 3664
33.3%
1 60
 
0.5%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7266
66.1%
. 3664
33.3%
1 60
 
0.5%
3 2
 
< 0.1%

html_num_tags('object')
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028930131
Minimum0
Maximum8
Zeros3579
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:35.618149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2376823
Coefficient of variation (CV)8.2157354
Kurtosis435.46225
Mean0.028930131
Median Absolute Deviation (MAD)0
Skewness16.802124
Sum106
Variance0.056492876
MonotonicityNot monotonic
2023-03-07T17:28:35.691898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3579
97.7%
1 76
 
2.1%
2 5
 
0.1%
3 1
 
< 0.1%
8 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 3579
97.7%
1 76
 
2.1%
2 5
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 5
 
0.1%
1 76
 
2.1%
0 3579
97.7%

html_num_tags('div')
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct302
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.843886
Minimum0
Maximum19941
Zeros328
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:35.799286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median33
Q362
95-th percentile251
Maximum19941
Range19941
Interquartile range (IQR)56

Descriptive statistics

Standard deviation365.5933
Coefficient of variation (CV)5.1605484
Kurtosis2399.0481
Mean70.843886
Median Absolute Deviation (MAD)28
Skewness45.205235
Sum259572
Variance133658.46
MonotonicityNot monotonic
2023-03-07T17:28:35.903946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 328
 
9.0%
41 322
 
8.8%
4 148
 
4.0%
1 144
 
3.9%
2 136
 
3.7%
8 93
 
2.5%
36 80
 
2.2%
3 74
 
2.0%
5 72
 
2.0%
32 71
 
1.9%
Other values (292) 2196
59.9%
ValueCountFrequency (%)
0 328
9.0%
1 144
3.9%
2 136
3.7%
3 74
 
2.0%
4 148
4.0%
5 72
 
2.0%
6 37
 
1.0%
7 31
 
0.8%
8 93
 
2.5%
9 63
 
1.7%
ValueCountFrequency (%)
19941 1
 
< 0.1%
5511 1
 
< 0.1%
2992 1
 
< 0.1%
2087 4
0.1%
1999 1
 
< 0.1%
1604 1
 
< 0.1%
1234 1
 
< 0.1%
1122 1
 
< 0.1%
956 2
0.1%
950 1
 
< 0.1%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
1.0
3590 
0.0
 
41
2.0
 
32
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10992
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3590
98.0%
0.0 41
 
1.1%
2.0 32
 
0.9%
3.0 1
 
< 0.1%

Length

2023-03-07T17:28:36.000622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T17:28:36.093308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3590
98.0%
0.0 41
 
1.1%
2.0 32
 
0.9%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 3705
33.7%
. 3664
33.3%
1 3590
32.7%
2 32
 
0.3%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7328
66.7%
Other Punctuation 3664
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3705
50.6%
1 3590
49.0%
2 32
 
0.4%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 3664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3705
33.7%
. 3664
33.3%
1 3590
32.7%
2 32
 
0.3%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3705
33.7%
. 3664
33.3%
1 3590
32.7%
2 32
 
0.3%
3 1
 
< 0.1%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
1.0
3495 
2.0
 
106
0.0
 
57
3.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10992
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3495
95.4%
2.0 106
 
2.9%
0.0 57
 
1.6%
3.0 6
 
0.2%

Length

2023-03-07T17:28:36.170052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T17:28:36.260000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3495
95.4%
2.0 106
 
2.9%
0.0 57
 
1.6%
3.0 6
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 3721
33.9%
. 3664
33.3%
1 3495
31.8%
2 106
 
1.0%
3 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7328
66.7%
Other Punctuation 3664
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3721
50.8%
1 3495
47.7%
2 106
 
1.4%
3 6
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 3664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3721
33.9%
. 3664
33.3%
1 3495
31.8%
2 106
 
1.0%
3 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3721
33.9%
. 3664
33.3%
1 3495
31.8%
2 106
 
1.0%
3 6
 
0.1%

html_num_tags('form')
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0289301
Minimum0
Maximum57
Zeros1191
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:36.330779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum57
Range57
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5639026
Coefficient of variation (CV)1.5199308
Kurtosis472.44134
Mean1.0289301
Median Absolute Deviation (MAD)1
Skewness15.345319
Sum3770
Variance2.4457913
MonotonicityNot monotonic
2023-03-07T17:28:36.401672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 1790
48.9%
0 1191
32.5%
2 433
 
11.8%
3 132
 
3.6%
4 49
 
1.3%
5 40
 
1.1%
7 12
 
0.3%
19 5
 
0.1%
8 4
 
0.1%
6 4
 
0.1%
Other values (4) 4
 
0.1%
ValueCountFrequency (%)
0 1191
32.5%
1 1790
48.9%
2 433
 
11.8%
3 132
 
3.6%
4 49
 
1.3%
5 40
 
1.1%
6 4
 
0.1%
7 12
 
0.3%
8 4
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
57 1
 
< 0.1%
19 5
 
0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 4
 
0.1%
7 12
 
0.3%
6 4
 
0.1%
5 40
1.1%
4 49
1.3%

html_num_tags('a')
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct300
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.18286
Minimum0
Maximum13451
Zeros672
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-03-07T17:28:36.493534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median16
Q352
95-th percentile238
Maximum13451
Range13451
Interquartile range (IQR)50

Descriptive statistics

Standard deviation342.65146
Coefficient of variation (CV)5.1773444
Kurtosis1244.8768
Mean66.18286
Median Absolute Deviation (MAD)16
Skewness32.572118
Sum242494
Variance117410.02
MonotonicityNot monotonic
2023-03-07T17:28:36.600727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 672
 
18.3%
3 214
 
5.8%
16 208
 
5.7%
1 186
 
5.1%
15 118
 
3.2%
4 117
 
3.2%
18 110
 
3.0%
2 107
 
2.9%
29 85
 
2.3%
5 80
 
2.2%
Other values (290) 1767
48.2%
ValueCountFrequency (%)
0 672
18.3%
1 186
 
5.1%
2 107
 
2.9%
3 214
 
5.8%
4 117
 
3.2%
5 80
 
2.2%
6 35
 
1.0%
7 60
 
1.6%
8 45
 
1.2%
9 37
 
1.0%
ValueCountFrequency (%)
13451 1
 
< 0.1%
13298 1
 
< 0.1%
2664 1
 
< 0.1%
2557 1
 
< 0.1%
2501 1
 
< 0.1%
2053 1
 
< 0.1%
1526 1
 
< 0.1%
1315 4
0.1%
1236 1
 
< 0.1%
909 1
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
0.0
3664 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10992
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3664
100.0%

Length

2023-03-07T17:28:36.696411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T17:28:36.781127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3664
100.0%

Most occurring characters

ValueCountFrequency (%)
0 7328
66.7%
. 3664
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7328
66.7%
Other Punctuation 3664
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7328
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7328
66.7%
. 3664
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7328
66.7%
. 3664
33.3%

label
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
benign
1858 
malicious
1806 

Length

Max length9
Median length6
Mean length7.4787118
Min length6

Characters and Unicode

Total characters27402
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmalicious
2nd rowbenign
3rd rowbenign
4th rowbenign
5th rowbenign

Common Values

ValueCountFrequency (%)
benign 1858
50.7%
malicious 1806
49.3%

Length

2023-03-07T17:28:36.854877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-07T17:28:36.947567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
benign 1858
50.7%
malicious 1806
49.3%

Most occurring characters

ValueCountFrequency (%)
i 5470
20.0%
n 3716
13.6%
b 1858
 
6.8%
e 1858
 
6.8%
g 1858
 
6.8%
m 1806
 
6.6%
a 1806
 
6.6%
l 1806
 
6.6%
c 1806
 
6.6%
o 1806
 
6.6%
Other values (2) 3612
13.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27402
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5470
20.0%
n 3716
13.6%
b 1858
 
6.8%
e 1858
 
6.8%
g 1858
 
6.8%
m 1806
 
6.6%
a 1806
 
6.6%
l 1806
 
6.6%
c 1806
 
6.6%
o 1806
 
6.6%
Other values (2) 3612
13.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 27402
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5470
20.0%
n 3716
13.6%
b 1858
 
6.8%
e 1858
 
6.8%
g 1858
 
6.8%
m 1806
 
6.6%
a 1806
 
6.6%
l 1806
 
6.6%
c 1806
 
6.6%
o 1806
 
6.6%
Other values (2) 3612
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27402
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5470
20.0%
n 3716
13.6%
b 1858
 
6.8%
e 1858
 
6.8%
g 1858
 
6.8%
m 1806
 
6.6%
a 1806
 
6.6%
l 1806
 
6.6%
c 1806
 
6.6%
o 1806
 
6.6%
Other values (2) 3612
13.2%

Interactions

2023-03-07T17:28:30.445497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.010477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:10.448970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:11.871526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:13.432214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.811046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.190334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:17.593693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.136736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.460846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.801132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:23.342670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.705823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:26.065961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:27.488812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:29.084754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:30.538185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.103168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:10.538271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:11.960885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:13.516708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.898535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.282028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:17.684165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.223446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.549549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.903788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:23.427391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.791536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:26.155664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:27.584266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:29.172443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:30.632229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.193861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:10.631972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:12.052583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:13.606409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.985242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.372725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:17.773865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.310160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.631827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.992547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:23.517109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.884230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:26.248355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:27.678950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:29.262146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:30.724917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.283275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:10.722669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:12.143496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:13.693118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:15.074058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.467174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:17.861578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.392795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.719446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:22.079082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:23.603812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.969939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:26.338446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:27.914919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:29.347925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:30.807652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.369981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:10.808158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:12.227982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:13.773845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:15.157778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.551891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:18.081835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.472533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.796860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:22.160808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:23.684542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.052667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:26.424608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.000637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:29.435634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:30.895348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.456162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:10.895870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:12.314692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:13.862549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:15.240501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.637604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:18.165555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.551705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.878388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:22.244529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:23.765276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.133394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:26.509095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.087343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:29.517360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:30.982059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.544863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:10.983572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:12.405392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:13.948261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:15.325219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.721325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:18.255255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.634780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.961112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:22.329247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:23.846714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.218597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:26.600792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.181030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:29.599960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:31.075744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.637491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:11.075269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:12.497081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.036968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:15.415915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.815011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:18.345952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.721489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.047826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:22.416952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:23.937411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.305307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:26.695471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.275714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:29.691653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:31.156474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.720389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:11.157988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:12.583796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.115705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:15.494651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.898450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:18.426684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.796239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.125562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:22.494769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.016147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.385322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:26.782182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.360430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:29.770395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:31.240194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.804120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:11.240723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:12.667517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.194443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:15.575382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.979917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:18.510401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.871986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.211276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:22.599421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.094883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.462813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:26.864908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.445147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:29.849126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:31.325881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.893825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:11.329426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:12.755450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.278158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:15.661094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:17.065631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:18.595461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.957700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.294003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:22.684136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.176997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.547032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:26.953666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.535592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:29.933600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:31.411794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:09.982529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:11.418133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:12.841161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.362879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:15.745812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:17.149180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:18.683168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.036445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.375718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:22.765867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.265369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.629750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:27.039569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.627285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:30.012337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:31.497193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:10.069238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:11.503757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:12.927887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.448589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:15.830528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:17.231903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:18.767890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.116119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.455205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:22.849583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.352381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.714466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:27.125283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.713994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:30.095062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:31.592930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:10.166923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:11.597446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:13.021574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.543272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:15.922221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:17.323596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:18.861523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.203834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.544906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:22.939286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.442823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.803811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:27.218717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.809674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:30.184762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:31.687713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:10.267577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:11.693123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:13.118251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.634969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.015908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:17.418280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:18.955209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.292671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.633814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:23.175230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.534361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.896106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:27.313398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.904357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:30.274468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:31.775198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:10.356280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:11.778836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:13.203967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:14.719353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:16.101631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:17.502996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:19.046039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:20.375133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:21.715304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:23.254965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:24.616417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:25.977258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:27.399112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:28.993060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-07T17:28:30.357183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-03-07T17:28:37.031291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
url_lenurl_num_hyphens_domurl_path_lenurl_domain_lenurl_hostname_lenurl_num_dotsurl_num_underscoresurl_query_lenurl_num_query_paraurl_entropyhtml_num_tags('iframe')html_num_tags('script')html_num_tags('object')html_num_tags('div')html_num_tags('form')html_num_tags('a')url_ip_presenturl_porthtml_num_tags('embed')html_num_tags('head')html_num_tags('body')label
url_len1.0000.0770.8470.1730.1800.3990.4040.3470.3350.8150.004-0.2040.002-0.211-0.010-0.2290.0000.0880.0000.0790.1360.060
url_num_hyphens_dom0.0771.000-0.1920.5530.556-0.141-0.1190.0880.0750.092-0.071-0.137-0.0720.1560.1370.0580.0840.0000.0000.0310.0710.280
url_path_len0.847-0.1921.000-0.234-0.2270.3680.4290.1500.1370.6880.029-0.1230.024-0.267-0.036-0.2230.0000.1610.0000.0830.0320.107
url_domain_len0.1730.553-0.2341.0000.9950.010-0.1710.0720.0730.157-0.030-0.072-0.0040.1340.1490.0440.1690.0000.0500.0600.0840.377
url_hostname_len0.1800.556-0.2270.9951.000-0.003-0.1690.0750.0760.164-0.029-0.060-0.0100.1470.1600.0570.2570.0000.0530.0610.0820.378
url_num_dots0.399-0.1410.3680.010-0.0031.0000.1750.0970.0990.3330.017-0.179-0.015-0.220-0.141-0.1630.0460.0000.0000.0000.0270.076
url_num_underscores0.404-0.1190.429-0.171-0.1690.1751.0000.1730.1680.351-0.023-0.119-0.041-0.194-0.177-0.1430.0320.3970.0000.0920.0150.144
url_query_len0.3470.0880.1500.0720.0750.0970.1731.0000.9220.360-0.040-0.129-0.024-0.0150.042-0.0970.0000.0000.0000.0000.2070.177
url_num_query_para0.3350.0750.1370.0730.0760.0990.1680.9221.0000.345-0.051-0.139-0.0200.0050.034-0.0770.0380.0000.0000.0000.2250.235
url_entropy0.8150.0920.6880.1570.1640.3330.3510.3600.3451.000-0.016-0.167-0.015-0.1240.024-0.1450.2710.0560.0250.0370.1460.256
html_num_tags('iframe')0.004-0.0710.029-0.030-0.0290.017-0.023-0.040-0.051-0.0161.0000.3070.1360.2450.1980.2700.0000.0000.0000.0320.0000.071
html_num_tags('script')-0.204-0.137-0.123-0.072-0.060-0.179-0.119-0.129-0.139-0.1670.3071.0000.0360.5550.3860.5950.0350.0000.0000.2000.0730.087
html_num_tags('object')0.002-0.0720.024-0.004-0.010-0.015-0.041-0.024-0.020-0.0150.1360.0361.0000.005-0.0480.1020.0760.0000.7530.0000.0990.120
html_num_tags('div')-0.2110.156-0.2670.1340.147-0.220-0.194-0.0150.005-0.1240.2450.5550.0051.0000.5320.8190.0000.0000.0000.0000.0000.036
html_num_tags('form')-0.0100.137-0.0360.1490.160-0.141-0.1770.0420.0340.0240.1980.386-0.0480.5321.0000.4570.0000.0000.0000.0000.0000.035
html_num_tags('a')-0.2290.058-0.2230.0440.057-0.163-0.143-0.097-0.077-0.1450.2700.5950.1020.8190.4571.0000.0000.0000.0000.0000.0000.036
url_ip_present0.0000.0840.0000.1690.2570.0460.0320.0000.0380.2710.0000.0350.0760.0000.0000.0001.0000.0970.0240.0090.0350.077
url_port0.0880.0000.1610.0000.0000.0000.3970.0000.0000.0560.0000.0000.0000.0000.0000.0000.0971.0000.0000.0000.0000.000
html_num_tags('embed')0.0000.0000.0000.0500.0530.0000.0000.0000.0000.0250.0000.0000.7530.0000.0000.0000.0240.0001.0000.0000.0000.110
html_num_tags('head')0.0790.0310.0830.0600.0610.0000.0920.0000.0000.0370.0320.2000.0000.0000.0000.0000.0090.0000.0001.0000.2750.000
html_num_tags('body')0.1360.0710.0320.0840.0820.0270.0150.2070.2250.1460.0000.0730.0990.0000.0000.0000.0350.0000.0000.2751.0000.133
label0.0600.2800.1070.3770.3780.0760.1440.1770.2350.2560.0710.0870.1200.0360.0350.0360.0770.0000.1100.0000.1331.000

Missing values

2023-03-07T17:28:31.924702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-07T17:28:32.197690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-07T17:28:32.502767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

url_lenurl_num_hyphens_domurl_path_lenurl_domain_lenurl_hostname_lenurl_num_dotsurl_num_underscoresurl_query_lenurl_num_query_paraurl_ip_presenturl_entropyurl_chinese_presenturl_porthtml_num_tags('iframe')html_num_tags('script')html_num_tags('embed')html_num_tags('object')html_num_tags('div')html_num_tags('head')html_num_tags('body')html_num_tags('form')html_num_tags('a')html_num_tags('applet')label
023.00.08.015.015.02.00.00.00.00.04.2603330.00.00.07.00.00.00.01.01.00.00.00.0malicious
175.00.058.017.017.06.00.00.00.00.04.6361070.00.00.018.00.00.020.01.01.00.021.00.0benign
220.00.04.016.016.02.00.00.00.00.03.7089660.00.01.033.00.00.0101.01.01.03.070.00.0benign
327.00.013.014.014.03.00.00.00.00.04.0255920.00.00.015.00.00.0151.01.01.01.055.00.0benign
439.02.012.027.027.02.00.00.00.00.04.6318330.00.00.010.00.00.0332.01.01.00.0321.00.0benign
518.00.00.018.018.02.00.00.00.00.03.9434650.00.00.04.01.01.03.01.01.00.018.00.0benign
649.00.030.019.019.04.00.00.00.00.04.2513650.00.00.08.00.00.019.01.01.01.04.00.0malicious
725.00.00.025.025.02.00.00.00.00.03.8903200.00.00.022.00.00.0333.01.01.01.0155.00.0benign
839.00.022.017.017.03.00.00.00.00.04.4171740.00.00.017.00.00.032.01.01.02.029.00.0benign
940.00.01.018.018.02.00.00.00.00.04.7720550.00.00.03.00.00.018.01.01.00.02.00.0malicious
url_lenurl_num_hyphens_domurl_path_lenurl_domain_lenurl_hostname_lenurl_num_dotsurl_num_underscoresurl_query_lenurl_num_query_paraurl_ip_presenturl_entropyurl_chinese_presenturl_porthtml_num_tags('iframe')html_num_tags('script')html_num_tags('embed')html_num_tags('object')html_num_tags('div')html_num_tags('head')html_num_tags('body')html_num_tags('form')html_num_tags('a')html_num_tags('applet')label
365463.00.049.014.014.01.00.00.00.00.04.6527370.00.00.01.00.00.02.01.02.01.00.00.0malicious
365525.00.014.011.011.02.00.00.00.00.03.9056390.00.00.014.00.00.036.01.01.00.041.00.0benign
3656126.00.046.017.017.02.05.062.02.00.05.0256470.00.00.014.00.00.046.01.01.01.00.00.0malicious
365742.00.021.021.021.01.00.00.00.00.04.1484150.00.00.063.00.00.017.01.01.01.045.00.0benign
365814.00.00.014.014.03.00.00.00.01.03.4992280.00.00.00.00.00.02.01.01.00.01.00.0benign
365968.03.016.052.052.02.00.00.00.00.04.1353560.00.00.00.00.00.011.01.01.00.03.00.0malicious
366066.00.048.018.018.02.00.00.00.00.04.3623310.00.01.014.00.00.0212.01.01.03.0475.00.0benign
366190.01.064.026.026.04.00.00.00.00.04.6933430.00.00.013.00.00.075.01.01.02.0103.00.0malicious
366246.00.033.013.013.03.00.00.00.00.04.6041660.00.00.00.00.00.04.01.01.00.03.00.0benign
366318.00.00.018.018.02.00.00.00.00.03.6194710.00.00.03.00.00.0282.01.01.02.046.00.0benign

Duplicate rows

Most frequently occurring

url_lenurl_num_hyphens_domurl_path_lenurl_domain_lenurl_hostname_lenurl_num_dotsurl_num_underscoresurl_query_lenurl_num_query_paraurl_ip_presenturl_entropyurl_chinese_presenturl_porthtml_num_tags('iframe')html_num_tags('script')html_num_tags('embed')html_num_tags('object')html_num_tags('div')html_num_tags('head')html_num_tags('body')html_num_tags('form')html_num_tags('a')html_num_tags('applet')label# duplicates
6719.00.06.013.013.02.00.00.00.00.03.9500640.00.00.06.00.00.038.01.01.01.018.00.0benign5
11326.00.012.014.014.02.00.00.00.00.03.9957150.00.00.07.00.00.051.01.01.01.0198.00.0benign5
12227.01.00.027.027.02.00.00.00.00.04.2783520.00.00.09.00.00.0200.01.01.02.0122.00.0benign5
24544.00.027.017.017.01.00.00.00.00.04.1394680.00.00.02.00.00.03.01.01.01.00.00.0malicious5
513.00.00.013.013.02.00.00.00.00.03.6841840.00.00.018.00.00.0219.01.01.01.0344.00.0benign4
813.00.00.013.013.02.00.00.00.00.03.8841840.00.00.00.00.00.03.01.01.00.00.00.0benign4
2316.00.00.016.016.02.00.00.00.00.03.6539970.00.01.016.00.00.079.01.01.01.0161.00.0benign4
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